{"title":"基于 ESO 的分数阶滑动模式控制器用于具有不匹配干扰的降压转换器:设计与实验","authors":"Shengquan Li;Hao Lu;Juan Li;Tao Zheng;Yu He","doi":"10.1109/TIE.2024.3525110","DOIUrl":null,"url":null,"abstract":"A novel fractional-order sliding mode control method based on a fractional-order extended state observer (FOSMC+FOESO) controller is developed to attenuate the matched and mismatched disturbances of a buck converter, i.e., load mutation and model errors. First, a novel fractional-order buck converter with matched and mismatched disturbances which can be derived from each other is proposed. The FOESO is designed to estimate and compensate for the mismatched disturbance and indirectly estimate the matched disturbance, which can reduce the sensitivity of the matched and mismatched disturbances. According to the proposed fractional-order model, the fractional-order sliding mode surface with the disturbance estimation term and exponential approach rate are introduced to improve the antidisturbance ability and voltage tracking performance of the system. The system stability is verified by a Lyapunov function with the Leibniz rule of fractional calculus. Finally, the comparison of several simulation and experimental results illustrates that the proposed FOSMC+FOESO controller has the superior voltage tracking performances than traditional sliding mode control (SMC), FOSMC, and SMC based on ESO methods.","PeriodicalId":13402,"journal":{"name":"IEEE Transactions on Industrial Electronics","volume":"72 8","pages":"8451-8462"},"PeriodicalIF":7.2000,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fractional-Order Sliding Mode Controller Based on ESO for a Buck Converter With Mismatched Disturbances: Design and Experiments\",\"authors\":\"Shengquan Li;Hao Lu;Juan Li;Tao Zheng;Yu He\",\"doi\":\"10.1109/TIE.2024.3525110\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A novel fractional-order sliding mode control method based on a fractional-order extended state observer (FOSMC+FOESO) controller is developed to attenuate the matched and mismatched disturbances of a buck converter, i.e., load mutation and model errors. First, a novel fractional-order buck converter with matched and mismatched disturbances which can be derived from each other is proposed. The FOESO is designed to estimate and compensate for the mismatched disturbance and indirectly estimate the matched disturbance, which can reduce the sensitivity of the matched and mismatched disturbances. According to the proposed fractional-order model, the fractional-order sliding mode surface with the disturbance estimation term and exponential approach rate are introduced to improve the antidisturbance ability and voltage tracking performance of the system. The system stability is verified by a Lyapunov function with the Leibniz rule of fractional calculus. Finally, the comparison of several simulation and experimental results illustrates that the proposed FOSMC+FOESO controller has the superior voltage tracking performances than traditional sliding mode control (SMC), FOSMC, and SMC based on ESO methods.\",\"PeriodicalId\":13402,\"journal\":{\"name\":\"IEEE Transactions on Industrial Electronics\",\"volume\":\"72 8\",\"pages\":\"8451-8462\"},\"PeriodicalIF\":7.2000,\"publicationDate\":\"2025-01-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Industrial Electronics\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10840210/\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Industrial Electronics","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10840210/","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Fractional-Order Sliding Mode Controller Based on ESO for a Buck Converter With Mismatched Disturbances: Design and Experiments
A novel fractional-order sliding mode control method based on a fractional-order extended state observer (FOSMC+FOESO) controller is developed to attenuate the matched and mismatched disturbances of a buck converter, i.e., load mutation and model errors. First, a novel fractional-order buck converter with matched and mismatched disturbances which can be derived from each other is proposed. The FOESO is designed to estimate and compensate for the mismatched disturbance and indirectly estimate the matched disturbance, which can reduce the sensitivity of the matched and mismatched disturbances. According to the proposed fractional-order model, the fractional-order sliding mode surface with the disturbance estimation term and exponential approach rate are introduced to improve the antidisturbance ability and voltage tracking performance of the system. The system stability is verified by a Lyapunov function with the Leibniz rule of fractional calculus. Finally, the comparison of several simulation and experimental results illustrates that the proposed FOSMC+FOESO controller has the superior voltage tracking performances than traditional sliding mode control (SMC), FOSMC, and SMC based on ESO methods.
期刊介绍:
Journal Name: IEEE Transactions on Industrial Electronics
Publication Frequency: Monthly
Scope:
The scope of IEEE Transactions on Industrial Electronics encompasses the following areas:
Applications of electronics, controls, and communications in industrial and manufacturing systems and processes.
Power electronics and drive control techniques.
System control and signal processing.
Fault detection and diagnosis.
Power systems.
Instrumentation, measurement, and testing.
Modeling and simulation.
Motion control.
Robotics.
Sensors and actuators.
Implementation of neural networks, fuzzy logic, and artificial intelligence in industrial systems.
Factory automation.
Communication and computer networks.